Renewable Energy-Supplying cooperatives (REScoops) are cooperatives of renewable energy producers and/or consumers, which are under formulation in the emerging European smart grid. Their emergence highlights the importance of proconsuming green energy and simultaneously puts forward principles such as energy democracy and self-consumption, assists the fight against energy poverty, and helps reduce GHG emissions. To this end, the incorporation of scientific and technological solutions into the REScoops’ everyday business and practices, is key for improving these practices and assessing their potential benefits, and as such for enabling them to deliver the maximum possible gains to their members and society at large. This chapter outlines three key axes of scientific research and solutions that can be used for REScoops, namely, (a) a statistical analysis, (b) an applied behavioural analysis, and (c) an artificial intelligence/machine learning one. Also presented are results and lessons learned from providing such solutions to European REScoops as part of the H2020 REScoop Plus project.

REScoops are cooperatives of renewable energy producers and/or consumers, which have begun to form in the emerging European Smart Grid. According to the Federation of REScoops in Europe (REScoop.eu), there are currently more than 2,397 REScoops, collectively having more than 650,000 members. As such, their emergence highlights the importance of producing and consuming green energy, and simultaneously puts forward principles such as energy democracy and self-consumption, assists the fight against energy poverty, and reduces Greenhouse Gas emissions. A core objective of the H2020 REScoop Plus project is to promote a better understanding and cultivate the behavioural change of the cooperatives’ engagement. In order for this goal to be met, it was essential to conduct a careful logging of and statistical analysis over the energy data stored by the REScoops, as well as the current state of their engagement in energy efficiency. Such tasks are imperative in order to support the claim that REScoop engagement promotes energy sobriety; and associate consumption reduction with specific behaviours, ICT tools, and Energy Efficiency practices. In this paper, we present the logging and statistical analysis conducted in REScoop Plus, and their results. Specifically, we outline the methodology used for formulating a comprehensive format for collecting the datasets for statistical analysis, a process that involved a lengthy and fruitful interaction with REScoops and their data experts; the extracted requirements for that common format; and the detailed guidelines communicated to the REScoops in order to enable data acquisition and analysis. Finally, we present a summary of the collected data (relating to electricity and heating consumption, demographics, etc.); the detailed statistical analysis process used; and the results of this analysis.